Background: Cancer is the most common malignancy in men and women globally. The
tyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intracellular
signal transduction processes and play a key role in cell proliferation, differentiation,
migration, metabolism, and programmed cell deaths. In this context, kinases are considered as a
potential drug target for cancer therapy.
Methods: In the present study, a two-dimensional (2D) quantitative structure-activity relationship
(2D-QSAR) was performed to analyze anticancer activities of 28 quinazolinyl-arylurea (QZA)
derivatives based on the liver (BEL-7402), stomach (MGC-803), and colon (HCC-827) cancer cell
lines using multiple linear regression (MLR) analysis. It was accomplished using 2D-QSAR
analysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors to
develop predictive models that correlate structural features of QZA derivatives to their anticancer
activities. A suitable set of molecular descriptors, such as constitutional, topological, geometrical,
electrostatic, and quantum-chemical descriptors were calculated to represent the structural features
of compounds. The genetic algorithm (GA) method was used to identify the important molecular
descriptors to build the QSAR models and used to predict the anti-cancer activities.
Results and Discussion: The obtained 2D-QSAR models were vigorously validated using various
statistical metrics using leave-one-out (LOO) and external test set prediction approaches. The best
predictive models by MLR gave highly significant square of correlation coefficient (R2
of 0.799, 0.815, and 0.779 for the training set, and the correlation coefficients (R2
test) were obtained
0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. The
models also demonstrated good predictive power confirmed by the high value of cross-validated
correlation coefficient Q2 value of 0.663, 0.717, and 0.671 for three different cancer cell lines.
Importantly, the model's quality was judged as well based on mean absolute error (MAE) criteria,
and the results were consistent with proposed limits by Golbraikh and Tropsha.
Conclusion: The QSAR results of the study indicated that the proposed models were robust and
free from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, and
ETA_Beta_ns_d have positively contributed descriptors for anti-cancer activity in the liver,
stomach, and colon cancer cell lines and a detailed mechanistic interpretation of each model
revealed important structural features that were responsible for favorable or unfavorable for anticancer
activity. The predictive ability of the proposed models was good and may be useful for
developing more potent quinazolinyl-arylurea compounds as anti-cancer agents.